Testing AutoChunk Memory Optimization for UNet Models in ColossalAI

hpcaitech/colossalai

tests/test_autochunk/test_autochunk_diffuser/benchmark_autochunk_diffuser.py

            
import time
from typing import Any

import torch
import torch.fx

import colossalai
from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
from colossalai.fx.graph_module import ColoGraphModule
from colossalai.fx.passes.meta_info_prop import MetaInfoProp
from colossalai.fx.profiler import parameter_size
from colossalai.utils import free_port

if AUTOCHUNK_AVAILABLE:
    from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen
    from colossalai.fx.profiler import MetaTensor
    from colossalai.fx.tracer.experimental import ColoTracer, symbolic_trace


def _benchmark_autochunk_unet_gm(
    model: Any,
    data: tuple,
    max_memory: int = None,
) -> None:
    model = model.cuda().eval()

    # build model and input
    meta_args, concrete_args = data
    if concrete_args is None:
        concrete_args = {}

    # trace the meta graph and setup codegen
    meta_graph = symbolic_trace(
        model,
        meta_args={k: v.to(torch.device("meta")) for k, v in meta_args},
        concrete_args={k: v for k, v in concrete_args},
    )
    interp = MetaInfoProp(meta_graph)
    meta_tensors = [i[1] for i in meta_args] + [i[1] for i in concrete_args]
    meta_tensors = [MetaTensor(i, fake_device="cpu") if isinstance(i, torch.Tensor) else i for i in meta_tensors]
    interp.propagate(*meta_tensors)
    codegen = AutoChunkCodeGen(
        meta_graph,
        max_memory=max_memory,
    )

    # trace and recompile
    # MetaInfoProp requires symbolic_trace but CodeGen requires ColoTracer
    graph = ColoTracer().trace(
        model.cuda().eval(),
        meta_args={k: v.to(torch.device("meta")) for k, v in meta_args},
        concrete_args={k: v for k, v in concrete_args},
    )
    graph.set_codegen(codegen)
    gm = ColoGraphModule(model, graph, ckpt_codegen=False)
    gm.recompile()

    # init inputs
    inputs = [i[1] for i in meta_args] + [i[1] for i in concrete_args]
    inputs = [i.cuda() if isinstance(i, torch.Tensor) else i for i in inputs]
    model.cuda().eval()

    # bench
    para_mem = float(parameter_size(model)) / 1024**2
    act_mem = _benchmark_memory(gm, inputs)
    speed = _benchmark_speed(gm, inputs)
    print(
        "unet autochunk, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB"
        % (speed, act_mem, para_mem, act_mem + para_mem)
    )


def _benchmark_autochunk_unet_origin(
    model: Any,
    data: tuple,
) -> None:
    # build model and input
    meta_args, concrete_args = data
    if concrete_args is None:
        concrete_args = {}

    # init inputs
    inputs = [i[1] for i in meta_args] + [i[1] for i in concrete_args]
    inputs = [i.cuda() if isinstance(i, torch.Tensor) else i for i in inputs]
    model.cuda().eval()

    # bench
    para_mem = float(parameter_size(model)) / 1024**2
    act_mem = _benchmark_memory(model, inputs)
    speed = _benchmark_speed(model, inputs)
    print(
        "unet origin, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB"
        % (speed, act_mem, para_mem, act_mem + para_mem)
    )
    return act_mem


def _benchmark_memory(model, inputs):
    with torch.no_grad():
        torch.cuda.reset_peak_memory_stats()
        now_mem = float(torch.cuda.memory_allocated()) / 1024**2
        model(*inputs)
        new_max_mem = float(torch.cuda.max_memory_allocated()) / 1024**2
    return new_max_mem - now_mem


def _benchmark_speed(model, inputs, loop=5):
    with torch.no_grad():
        for _ in range(loop // 2 + 1):
            model(*inputs)
        torch.cuda.synchronize()
        time1 = time.time()
        for _ in range(loop):
            model(*inputs)
        torch.cuda.synchronize()
        time2 = time.time()
    return (time2 - time1) / loop


def benchmark_autochunk_unet(batch=1, height=448, width=448):
    from test_autochunk_unet import UNet2DModel, get_data

    model = UNet2DModel()
    latent_shape = (batch, 3, height // 7, width // 7)

    print("
batch: %d, height: %d, width: %d" % (batch, height, width))
    max_mem = _benchmark_autochunk_unet_origin(model, get_data(latent_shape))
    for ratio in [0.5, 0.4, 0.3, 0.2]:
        try:
            _benchmark_autochunk_unet_gm(model, get_data(latent_shape), max_mem * ratio)
        except RuntimeError as e:
            if e.args[0] == "Search failed. Try a larger memory threshold.":
                break
        except Exception as e:
            raise e
    _benchmark_autochunk_unet_gm(model, get_data(latent_shape), None)


if __name__ == "__main__":
    # launch colossalai
    colossalai.launch(
        config={},
        rank=0,
        world_size=1,
        host="localhost",
        port=free_port(),
        backend="nccl",
    )
    benchmark_autochunk_unet(batch=1, height=224 * 3, width=224 * 3)
    benchmark_autochunk_unet(batch=1, height=224 * 4, width=224 * 4)
    benchmark_autochunk_unet(batch=1, height=224 * 5, width=224 * 5)
    benchmark_autochunk_unet(batch=1, height=224 * 6, width=224 * 6)